Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "48"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 48 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 48, Node N06:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460009 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.305786 1.168818 -0.325643 1.610320 -1.016927 2.223608 -0.601211 -1.909023 0.5717 0.5930 0.3581 nan nan
2460008 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.091841 1.291554 -0.339463 1.907531 -0.721738 1.869974 -0.668772 0.474583 0.6229 0.6398 0.3237 nan nan
2460007 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.012414 0.911010 -0.545265 1.400825 1.348095 1.414839 -0.625062 -2.060348 0.5761 0.6002 0.3453 nan nan
2459999 not_connected 0.00% 89.97% 85.96% 0.00% - - nan nan nan nan nan nan nan nan 0.1491 0.1719 0.0856 nan nan
2459998 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.133336 0.815620 -0.350076 1.384748 -1.040652 2.396548 -0.672886 -1.943562 0.5687 0.5946 0.3762 nan nan
2459997 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.081753 0.755540 -0.106474 1.507832 -1.024270 2.071446 -0.392997 -3.108375 0.5980 0.6228 0.3848 nan nan
2459996 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.073028 1.118047 -0.337671 1.644562 -1.178603 1.945032 -0.750841 -1.447168 0.5980 0.6192 0.3978 nan nan
2459995 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.009896 0.894489 -0.324555 1.666186 -1.046480 2.277508 -0.813605 -1.789803 0.5986 0.6222 0.3834 nan nan
2459994 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.124332 0.792943 -0.273037 1.516176 -1.108793 1.926993 -0.750606 -1.981585 0.5892 0.6146 0.3795 nan nan
2459993 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.516058 1.443162 -0.056784 1.760495 -0.854307 2.293846 -0.429399 -1.734360 0.5816 0.6248 0.3930 nan nan
2459991 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.575933 1.135149 -0.189094 1.795240 -0.751342 2.236178 -0.301760 -1.827805 0.5877 0.6086 0.3880 nan nan
2459990 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.420048 1.110711 -0.219910 1.915465 -0.746005 2.445525 0.213195 -1.713274 0.5870 0.6130 0.3882 nan nan
2459989 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.198505 1.023737 0.006582 1.670375 -1.001253 1.578307 -0.479915 -2.016665 0.5851 0.6126 0.3898 nan nan
2459988 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.402914 1.434347 -0.200704 1.964339 -0.983071 2.890034 -0.603398 -1.737267 0.5727 0.5979 0.3732 nan nan
2459987 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.134490 0.814139 -0.230716 1.507241 -0.801897 1.506969 0.505154 -0.669629 0.5956 0.6181 0.3757 nan nan
2459986 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.402236 1.382886 -0.231229 1.871140 -0.975592 2.579377 -0.773249 0.764554 0.6022 0.6270 0.3415 nan nan
2459985 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.057692 1.578542 -0.350049 1.471238 1.070859 1.280298 -0.497144 -2.752312 0.5877 0.6084 0.3798 nan nan
2459984 not_connected 100.00% 100.00% 0.00% 0.00% - - 12.980854 7.045926 4.346316 1.340158 9.593825 2.800261 4.548056 1.711592 0.0402 0.5970 0.4510 nan nan
2459983 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.002944 0.854672 0.545468 1.803028 -0.304304 2.593406 -0.294674 -0.476995 0.6258 0.6489 0.3134 nan nan
2459982 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.131301 0.111655 -0.088910 1.036449 -0.750819 -0.158411 -0.864113 -0.349317 0.6574 0.6626 0.3028 nan nan
2459981 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.033850 0.857183 0.640668 2.205754 -0.309054 2.717260 -0.677211 -2.158420 0.5908 0.6097 0.3790 nan nan
2459980 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.135947 0.588360 0.372992 1.460203 -0.826730 1.680711 -0.741532 0.207087 0.6358 0.6466 0.3189 nan nan
2459979 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.081518 0.724425 0.363546 1.514790 -0.889184 1.518431 -1.134690 -2.151575 0.5875 0.6107 0.3812 nan nan
2459978 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.009708 0.774318 0.505880 1.812502 -0.966202 2.166310 -1.401370 -2.527830 0.5839 0.6054 0.3883 nan nan
2459977 not_connected 0.00% 0.00% 0.00% 0.00% - - -0.028555 0.992398 0.369998 1.433789 -0.148479 1.852183 -1.202901 -2.162140 0.5548 0.5718 0.3456 nan nan
2459976 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.068475 0.818236 0.352584 1.733252 -0.485328 2.348420 -0.476889 -1.878955 0.5980 0.6170 0.3840 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 48: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.223608 -0.305786 1.168818 -0.325643 1.610320 -1.016927 2.223608 -0.601211 -1.909023

Antenna 48: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Power 1.907531 1.291554 0.091841 1.907531 -0.339463 1.869974 -0.721738 0.474583 -0.668772

Antenna 48: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 1.414839 0.012414 0.911010 -0.545265 1.400825 1.348095 1.414839 -0.625062 -2.060348

Antenna 48: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Shape nan nan nan nan nan nan nan nan nan

Antenna 48: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.396548 -0.133336 0.815620 -0.350076 1.384748 -1.040652 2.396548 -0.672886 -1.943562

Antenna 48: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.071446 -0.081753 0.755540 -0.106474 1.507832 -1.024270 2.071446 -0.392997 -3.108375

Antenna 48: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 1.945032 -0.073028 1.118047 -0.337671 1.644562 -1.178603 1.945032 -0.750841 -1.447168

Antenna 48: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.277508 -0.009896 0.894489 -0.324555 1.666186 -1.046480 2.277508 -0.813605 -1.789803

Antenna 48: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 1.926993 0.124332 0.792943 -0.273037 1.516176 -1.108793 1.926993 -0.750606 -1.981585

Antenna 48: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.293846 0.516058 1.443162 -0.056784 1.760495 -0.854307 2.293846 -0.429399 -1.734360

Antenna 48: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.236178 0.575933 1.135149 -0.189094 1.795240 -0.751342 2.236178 -0.301760 -1.827805

Antenna 48: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.445525 1.110711 0.420048 1.915465 -0.219910 2.445525 -0.746005 -1.713274 0.213195

Antenna 48: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Power 1.670375 1.023737 0.198505 1.670375 0.006582 1.578307 -1.001253 -2.016665 -0.479915

Antenna 48: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.890034 1.434347 0.402914 1.964339 -0.200704 2.890034 -0.983071 -1.737267 -0.603398

Antenna 48: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Power 1.507241 -0.134490 0.814139 -0.230716 1.507241 -0.801897 1.506969 0.505154 -0.669629

Antenna 48: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.579377 1.382886 0.402236 1.871140 -0.231229 2.579377 -0.975592 0.764554 -0.773249

Antenna 48: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Shape 1.578542 1.578542 0.057692 1.471238 -0.350049 1.280298 1.070859 -2.752312 -0.497144

Antenna 48: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected ee Shape 12.980854 12.980854 7.045926 4.346316 1.340158 9.593825 2.800261 4.548056 1.711592

Antenna 48: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.593406 0.002944 0.854672 0.545468 1.803028 -0.304304 2.593406 -0.294674 -0.476995

Antenna 48: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Power 1.036449 -0.131301 0.111655 -0.088910 1.036449 -0.750819 -0.158411 -0.864113 -0.349317

Antenna 48: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.717260 0.857183 0.033850 2.205754 0.640668 2.717260 -0.309054 -2.158420 -0.677211

Antenna 48: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 1.680711 0.588360 -0.135947 1.460203 0.372992 1.680711 -0.826730 0.207087 -0.741532

Antenna 48: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 1.518431 0.081518 0.724425 0.363546 1.514790 -0.889184 1.518431 -1.134690 -2.151575

Antenna 48: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.166310 0.774318 0.009708 1.812502 0.505880 2.166310 -0.966202 -2.527830 -1.401370

Antenna 48: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 1.852183 -0.028555 0.992398 0.369998 1.433789 -0.148479 1.852183 -1.202901 -2.162140

Antenna 48: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
48 N06 not_connected nn Temporal Variability 2.348420 0.818236 0.068475 1.733252 0.352584 2.348420 -0.485328 -1.878955 -0.476889
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